up

src/diffusers/models/transformers/transformer_hunyuanimage.py

@@ -754,7 +754,7 @@ class HunyuanImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
         # set recursively
         processors = {}
 
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: dict[str, AttentionProcessor]):
             if hasattr(module, "get_processor"):
                 processors[f"{name}.processor"] = module.get_processor()
 

src/diffusers/pipelines/hunyuan_image/pipeline_hunyuanimage.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import inspect
 import re
 from typing import Any, Callable, Dict, List, Optional, Union
@@ -99,9 +101,9 @@ def extract_glyph_text(prompt: str):
 def retrieve_timesteps(
     scheduler,
     num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    sigmas: Optional[List[float]] = None,
+    device: Optional[str | torch.device] = None,
+    timesteps: Optional[list[int]] = None,
+    sigmas: Optional[list[float]] = None,
     **kwargs,
 ):
     r"""
@@ -116,15 +118,15 @@ def retrieve_timesteps(
             must be `None`.
         device (`str` or `torch.device`, *optional*):
             The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        timesteps (`List[int]`, *optional*):
+        timesteps (`list[int]`, *optional*):
             Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
             `num_inference_steps` and `sigmas` must be `None`.
-        sigmas (`List[float]`, *optional*):
+        sigmas (`list[float]`, *optional*):
             Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
             `num_inference_steps` and `timesteps` must be `None`.
 
     Returns:
-        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        `tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
         second element is the number of inference steps.
     """
     if timesteps is not None and sigmas is not None:

src/diffusers/pipelines/hunyuan_image/pipeline_hunyuanimage_refiner.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import inspect
 from typing import Any, Callable, Dict, List, Optional, Union
 
@@ -63,9 +65,9 @@ EXAMPLE_DOC_STRING = """
 def retrieve_timesteps(
     scheduler,
     num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    sigmas: Optional[List[float]] = None,
+    device: Optional[str | torch.device] = None,
+    timesteps: Optional[list[int]] = None,
+    sigmas: Optional[list[float]] = None,
     **kwargs,
 ):
     r"""
@@ -80,15 +82,15 @@ def retrieve_timesteps(
             must be `None`.
         device (`str` or `torch.device`, *optional*):
             The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        timesteps (`List[int]`, *optional*):
+        timesteps (`list[int]`, *optional*):
             Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
             `num_inference_steps` and `sigmas` must be `None`.
-        sigmas (`List[float]`, *optional*):
+        sigmas (`list[float]`, *optional*):
             Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
             `num_inference_steps` and `timesteps` must be `None`.
 
     Returns:
-        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        `tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
         second element is the number of inference steps.
     """
     if timesteps is not None and sigmas is not None: